# Verify that TensorFlow While and StatelessWhile ops are mapped to the
# composite While op in MLIR with is_stateless attribute set accordingly to
# distinguish between them.

// RUN: tf-quant-opt %s -split-input-file -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc -inline -quant-prepare-lifting -quant-lift-quantizable-spots-as-functions='target-opset=XLA' | FileCheck %s

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# RUN: %p/debug_info | FileCheck %s

# pylint: disable=missing-docstring,line-too-long
import tensorflow.compat.v2 as tf
from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common


class TestModule(tf.Module):

TF_CAPI_EXPORT extern void TFE_DeleteTensorDebugInfo(
    TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info) {
  return debug_info->dev_dims.size();
}

TF_CAPI_EXPORT extern int64_t TFE_TensorDebugInfoOnDeviceDim(
    TFE_TensorDebugInfo* debug_info, int dim_index) {
  return debug_info->dev_dims[dim_index];
}

  TFE_TensorDebugInfo* debug_info = TFE_TensorHandleTensorDebugInfo(h, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  ASSERT_EQ(2, TFE_TensorDebugInfoOnDeviceNumDims(debug_info));
  // Shape is the same for CPU tensors.
  EXPECT_EQ(3, TFE_TensorDebugInfoOnDeviceDim(debug_info, 0));
  EXPECT_EQ(2, TFE_TensorDebugInfoOnDeviceDim(debug_info, 1));

  TFE_DeleteTensorDebugInfo(debug_info);

                            const GraphDebugInfo& debug_info)
      : meta_graph_def_(meta_graph_def), debug_info_(debug_info) {
    DCHECK(meta_graph_def);
  }

  virtual ~SavedModelMLIRImportInput();

  // The original MetaGraphDef of the savedmodel.
  const MetaGraphDef& meta_graph_def() const { return *meta_graph_def_; }

  const GraphDebugInfo& debug_info() const { return debug_info_; }

                      internal::FileExists(Env::Default(), debug_info_pb_path));
  if (debug_info_pb_exists) {
    GraphDebugInfo debug_info;
    TF_RETURN_IF_ERROR(
        ReadBinaryProto(Env::Default(), debug_info_pb_path, &debug_info));
    *debug_info_proto = std::make_unique<GraphDebugInfo>(std::move(debug_info));
  }
  return absl::OkStatus();
}

    srcs = ["duplicate_method_names_v1.py"],
    deps = [
        ":common_v1",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "debug_info",
    srcs = ["debug_info.py"],
    deps = [
        ":common",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "keras",
    srcs = ["keras.py"],
    deps = [

#include "tsl/platform/statusor.h"

namespace tensorflow {

absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result) {
  using ::tflite::optimize::ReducedPrecisionSupport;
  mlir::MLIRContext context;
  GraphImportConfig specs;
  mlir::quant::QuantizationSpecs quant_specs;

    // specifiable.
    GraphDebugInfo debug_info;
    switch (config.dialect) {
      case MlirDumpConfig::Dialect::kTFG: {
        TF_ASSIGN_OR_RETURN(module,
                            mlir::tfg::ImportGraphAndFunctionsToMlir(
                                &context, debug_info, graph,
                                flib_def ? *flib_def : graph.flib_def()));
        break;